基于人工神经网络的智能化CAPP系统应用

提出将人工神经网络中的ART1网络应用于智能化CAPP系统开发中,解决了传统CAPP系统在自动化、智能化方面的不足,提高了CAPP系统的智能推理和自动决策能力.开发了系统应用实例,证实该研究的可行性,为CAPP的进一步智能化提供了新的思路.     

基于爬虫的数据监控系统

近年来,随着互联网技术的快速发展,云计算服务开始全面普及,大型互联网公司以及中小型企业已经开始提供自己的云计算服务,中国科学院也提供了云计算服务平台——中科院“科技云”.本文针对中国科学院“科技云”项目的实际需求,参考已有商业云监控系统的功能和运行模式,设计并实现了一种基于爬虫的数据监控系统.该系统相比商业云监控系统,支持URL（布尔）类型数据监测的同时,增加了对数值类型、文本类型的监测,更好的支持第三方服务的监测,并实现了服务故障警报,监测数据可视化以及监测数据分布式存储.     

一种新的TCP拥塞控制慢启动策略

分析了现有的TCP拥塞控制慢启动策略及其存在的问题，提出了一种能使拥塞窗口平滑增长到慢启动阈值的新策略。NS仿真表明，新的慢启动策略能明显减少丢包数和突发数据量．     

一种基于双层进化结构的网格任务调度算法

在网格计算中,任务调度是一个重要的组成部分,并被证明为NP完全问题,以启发式方法求解较为适合。针对网格环境异构、分布等特点,本文运用遗传算法原理,结合高度分层排序,提出了一种基于双层进化结构的网格任务调度算法。模拟实验结果表明,在网格异构环境下,本算法优于其它几种算法。     

基于FPGA控制的IDE磁盘阵列设计

设计了一种基于FPGA控制的高速数据存储系统。该系统采用FPGA实现了对四个符合ATA-6规范的、RAID0配置的IDE磁盘阵列的管理,并配合四个SDRAM实现对数据的高速稳定存储。该磁盘阵列同时挂四个IDE硬盘,平均数据流达到200MB/s,峰值传输速率达到800MB/s,也可以扩展更多硬盘,构成大容量的磁盘阵列。     

STBC MC-CDMA系统自适应MBER多用户检测算法

针对MC-CDMA(Multicarrier code division multiple access)系统,描述了其时变多径衰落环境下采用空时分组编码(Space-time block coding,STBC)的同步上行链路模型,提出一种基于最小误比特率(Minimum biter-ror rate,MBER)的多用户检测算法。利用内核密度估计和梯度下降的方法,推导了该算法自适应实现形式。对具有2个发射天线和2个接收天线的STBC MC-CDMA同步上行链路系统进行了自适应MBER检测算法的计算机仿真。结果表明:当系统负荷较小时,该算法误码性能较好;当系统负荷较大时,该算法误码性能有所下降,但仍然优于具有类似计算复杂度的MMSE(Minimum mean squared error)自适应检测算法。     

Formulation of unified Jacobian for serial-parallel manipulators

This paper presents a general approach for Jacobian analysis of serial-parallel manipulators (S-PMs) formed by two lower mobility parallel manipulators (PMs) connected in serials. Based on the kinematic relation, coupling and constraint properties of each PM, the unified forward and inverse Jacobian matrices for S-PMs are derived in explicit form. It is shown that the Jacobian matrices of S-PMs have unified forms, which include the complete information of each PM. A (3-RPS)+(3-SPS/UP) S-PM is used as an example to demonstrate the proposed approach. The established model is applicable for S-PMs with various architectures.     

模糊自适应卡尔曼滤波算法在航位推算系统中的应用

针对实际的应用中车载航位推算系统的模型参数、噪声的统计特性不确定性,影响估计效果,提出了车载航位推算的模糊自适应卡尔曼滤波模型及其滤波算法;该方法通过监视理论残差与实际残差的比值是否接近1,应用模糊推理系统不断地调整量测噪声协方差的加权,对自适应卡尔曼滤波的量测噪声协方差进行递推修正,通过该算法来抑制噪声对精度的影响,进而提高系统的导航精度;仿真结果表明,这种算法能够有效地提高系统的精度,是一种比较理想的车载DR导航滤波方法。     

Parameter adaptation for generalized multiframe tasks: schedulability analysis,case study,and applications to self-suspending tasks

The generalized multiframe task model (GMF) extends the sporadic task model and multiframe task model. Each frame in the GMF model contains an execution time, a relative deadline, and a minimum inter-arrival time. These parameters are fixed after task specification time in the GMF model. However, multimedia and adaptive control systems may be overloaded and no longer stabilized when the task parameters in such systems are not flexible. In order to address this problem, deadlines and periods of frames may change to alleviate temporal overload, e.g., in the parameter adaptation and elastic scheduling model. In this paper, we propose a new model GMF-PA (the GMF model with parameter adaptation). This model allows task parameters to be flexible in arbitrary-deadline systems. A necessary schedulability test based on mixed-integer linear programming is given to check the schedulability under EDF scheduling and optimally assign frame deadlines and periods at the same time. We also prove that the test is a sufficient and necessary schedulability test when frame deadlines and periods must be integers. An approximation algorithm is also deployed to reduce computational running time and indicates a sufficient schedulability test in general. The speed-up factor of our approximation algorithm is \(1+\epsilon \) where \(\epsilon \) can be arbitrarily small, with respect to the exact schedulability test of GMF-PA tasks under EDF. We also apply the GMF model to self-suspending tasks. By extending recent work on scheduling self-suspending tasks, we remove the assumption that frame deadlines are equally assigned in self-suspending tasks, and the system is extended from constrained-deadline systems to arbitrary-deadline systems. We have done extensive experiments to show that the schedulability ratio is improved using our techniques in our GMF-PA model.     

关联规则挖掘最小支持度阀值设定的优化算法研究

为了解决在关联规则挖掘Apriori算法中最小支持度阀值设定较难或设定不适当的问题,基于事务普遍具有统计规律性的前提,设计了平均值调整优化的算法.为支持度和预选率划分不同区间,利用平均值进行最小支持度阀值的微调.对于不同分布特征的事务数据库,该方法比原先设定阀值都能取得更适中和更好的频繁项集结果,同时不会产生过大的性能...